What checks should we put around AI recommendations?

AI recommendations need an accountable human decision gate because AI is probabilistic and can produce convincing answers that miss the real-world context. A useful control is to make a named person or decision group responsible for reviewing the recommendation before it informs strategy, communications or customer-facing choices. Reviewers should ask whether the recommendation is accurate, relevant to the organisation's circumstances and fair to the people affected by it.

The point is not to slow every task down. AI can help teams search, collate, summarise and handle repetitive work at speed. The check belongs at the point where an output becomes a meaningful business decision. Human reviewers bring the empathy and contextual understanding needed to judge how a recommendation will land with customers, colleagues and communities.

AI recommendations are strongest when they support analysis and weakest when they are treated as a substitute for imagination, interpretation and strategic judgement. Generative AI can identify patterns, combine existing material and produce a starting point quickly. That can create useful momentum, especially when teams need to work through complex information.

The human check is to test what the recommendation leaves out. Ask whether the answer relies on familiar patterns, overlooks a cultural nuance or fails to make the lateral connections needed for a new strategic possibility. Bring together people with different knowledge of the market, customer and organisation to challenge the output. That turns AI from an authority to obey into a collaborator that gives the team more material to assess.

A polished AI recommendation can still be inaccurate, biased or poorly suited to the people it affects. Checks should therefore go beyond whether a recommendation sounds plausible. Test factual claims, inspect the assumptions behind the answer and assess whether images, language or proposed actions misread cultural context. Where a recommendation affects accessibility or inclusion, involve people who understand the lived experience rather than relying on automated judgement alone.

The level of review should match the consequence of the decision. Routine drafting can use lighter human review, while strategic choices, public communications and decisions that shape people's access to technology need closer scrutiny. Clear ownership, documented review and the ability to reject an AI output make the process more honest and easier to improve over time.

We believe AI recommendations should create possibilities for better thinking, not become a shortcut around it. AI can search, organise and accelerate the routine parts of complex work, but it cannot replace the human imagination needed to connect distant ideas or understand another person's experience. That is why we keep people at the centre of the work. At The Frameworks, we have argued for human oversight where AI can misinterpret context or reflect bias, particularly in accessibility. We have also been clear that generative AI is better at mixing known patterns than creating the unexpected connections that make stories, brands and strategic ideas resonate. The practical answer is simple: use AI as a collaborator, then give humans the authority and time to challenge its recommendations.